A fixed-rate MIMO CSI codec using vector quantization plus a codeword and side-information conditioned diffusion decoder outperforms existing neural CSI compressors in rate-distortion simulations.
An Efficient Network with Novel Quantization Designed for Massive MIMO CSI Feedback
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abstract
The efficacy of massive multiple-input multiple-output (MIMO) techniques heavily relies on the accuracy of channel state information (CSI) in frequency division duplexing (FDD) systems. Many works focus on CSI compression and quantization methods to enhance CSI reconstruction accuracy with lower feedback overhead. In this letter, we propose CsiConformer, a novel CSI feedback network that combines convolutional operations and self-attention mechanisms to improve CSI feedback accuracy. Additionally, a new quantization module is developed to improve encoding efficiency. Experiment results show that CsiConformer outperforms previous state-of-the-art networks, achieving an average accuracy improvement of 17.67\% with lower computational overhead.
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Generative Diffusion Model-based Compression of MIMO CSI
A fixed-rate MIMO CSI codec using vector quantization plus a codeword and side-information conditioned diffusion decoder outperforms existing neural CSI compressors in rate-distortion simulations.